[SPARK-6876] [PySpark] [SQL] add DataFrame na.replace in pyspark
Author: Daoyuan Wang <daoyuan.wang@intel.com> Closes #6003 from adrian-wang/pynareplace and squashes the following commits: 672efba [Daoyuan Wang] remove py2.7 feature 4a148f7 [Daoyuan Wang] to_replace support dict, value support single value, and add full tests 9e232e7 [Daoyuan Wang] rename scala map af0268a [Daoyuan Wang] remove na 63ac579 [Daoyuan Wang] add na.replace in pyspark
This commit is contained in:
Daoyuan Wang
Reynold Xin
parent
ec6f2a9774
commit
d86ce84584
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@ -53,4 +53,11 @@ private[spark] object PythonUtils {
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def toSeq[T](cols: JList[T]): Seq[T] = {
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cols.toList.toSeq
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}
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/**
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* Convert java map of K, V into Map of K, V (for calling API with varargs)
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*/
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def toScalaMap[K, V](jm: java.util.Map[K, V]): Map[K, V] = {
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jm.toMap
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}
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}
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@ -578,6 +578,10 @@ class DataFrame(object):
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"""Return a JVM Seq of Columns from a list of Column or names"""
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return _to_seq(self.sql_ctx._sc, cols, converter)
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def _jmap(self, jm):
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"""Return a JVM Scala Map from a dict"""
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return _to_scala_map(self.sql_ctx._sc, jm)
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def _jcols(self, *cols):
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"""Return a JVM Seq of Columns from a list of Column or column names
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@ -924,6 +928,80 @@ class DataFrame(object):
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return DataFrame(self._jdf.na().fill(value, self._jseq(subset)), self.sql_ctx)
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def replace(self, to_replace, value, subset=None):
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"""Returns a new :class:`DataFrame` replacing a value with another value.
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:param to_replace: int, long, float, string, or list.
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Value to be replaced.
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If the value is a dict, then `value` is ignored and `to_replace` must be a
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mapping from column name (string) to replacement value. The value to be
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replaced must be an int, long, float, or string.
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:param value: int, long, float, string, or list.
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Value to use to replace holes.
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The replacement value must be an int, long, float, or string. If `value` is a
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list or tuple, `value` should be of the same length with `to_replace`.
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:param subset: optional list of column names to consider.
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Columns specified in subset that do not have matching data type are ignored.
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For example, if `value` is a string, and subset contains a non-string column,
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then the non-string column is simply ignored.
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>>> df4.replace(10, 20).show()
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+----+------+-----+
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| age|height| name|
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+----+------+-----+
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| 20| 80|Alice|
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| 5| null| Bob|
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|null| null| Tom|
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|null| null| null|
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+----+------+-----+
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>>> df4.replace(['Alice', 'Bob'], ['A', 'B'], 'name').show()
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+----+------+----+
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| age|height|name|
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+----+------+----+
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| 10| 80| A|
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| 5| null| B|
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|null| null| Tom|
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|null| null|null|
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+----+------+----+
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"""
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if not isinstance(to_replace, (float, int, long, basestring, list, tuple, dict)):
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raise ValueError(
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"to_replace should be a float, int, long, string, list, tuple, or dict")
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if not isinstance(value, (float, int, long, basestring, list, tuple)):
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raise ValueError("value should be a float, int, long, string, list, or tuple")
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rep_dict = dict()
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if isinstance(to_replace, (float, int, long, basestring)):
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to_replace = [to_replace]
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if isinstance(to_replace, tuple):
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to_replace = list(to_replace)
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if isinstance(value, tuple):
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value = list(value)
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if isinstance(to_replace, list) and isinstance(value, list):
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if len(to_replace) != len(value):
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raise ValueError("to_replace and value lists should be of the same length")
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rep_dict = dict(zip(to_replace, value))
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elif isinstance(to_replace, list) and isinstance(value, (float, int, long, basestring)):
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rep_dict = dict([(tr, value) for tr in to_replace])
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elif isinstance(to_replace, dict):
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rep_dict = to_replace
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if subset is None:
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return DataFrame(self._jdf.na().replace('*', rep_dict), self.sql_ctx)
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elif isinstance(subset, basestring):
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subset = [subset]
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if not isinstance(subset, (list, tuple)):
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raise ValueError("subset should be a list or tuple of column names")
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return DataFrame(
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self._jdf.na().replace(self._jseq(subset), self._jmap(rep_dict)), self.sql_ctx)
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def corr(self, col1, col2, method=None):
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"""
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Calculates the correlation of two columns of a DataFrame as a double value. Currently only
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@ -1226,6 +1304,13 @@ def _to_seq(sc, cols, converter=None):
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return sc._jvm.PythonUtils.toSeq(cols)
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def _to_scala_map(sc, jm):
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"""
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Convert a dict into a JVM Map.
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"""
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return sc._jvm.PythonUtils.toScalaMap(jm)
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def _unary_op(name, doc="unary operator"):
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""" Create a method for given unary operator """
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def _(self):
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@ -665,6 +665,54 @@ class SQLTests(ReusedPySparkTestCase):
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result = df.select(functions.bitwiseNOT(df.b)).collect()[0].asDict()
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self.assertEqual(~75, result['~b'])
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def test_replace(self):
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schema = StructType([
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StructField("name", StringType(), True),
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StructField("age", IntegerType(), True),
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StructField("height", DoubleType(), True)])
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# replace with int
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row = self.sqlCtx.createDataFrame([(u'Alice', 10, 10.0)], schema).replace(10, 20).first()
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self.assertEqual(row.age, 20)
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self.assertEqual(row.height, 20.0)
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# replace with double
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row = self.sqlCtx.createDataFrame(
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[(u'Alice', 80, 80.0)], schema).replace(80.0, 82.1).first()
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self.assertEqual(row.age, 82)
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self.assertEqual(row.height, 82.1)
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# replace with string
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row = self.sqlCtx.createDataFrame(
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[(u'Alice', 10, 80.1)], schema).replace(u'Alice', u'Ann').first()
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self.assertEqual(row.name, u"Ann")
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self.assertEqual(row.age, 10)
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# replace with subset specified by a string of a column name w/ actual change
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row = self.sqlCtx.createDataFrame(
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[(u'Alice', 10, 80.1)], schema).replace(10, 20, subset='age').first()
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self.assertEqual(row.age, 20)
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# replace with subset specified by a string of a column name w/o actual change
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row = self.sqlCtx.createDataFrame(
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[(u'Alice', 10, 80.1)], schema).replace(10, 20, subset='height').first()
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self.assertEqual(row.age, 10)
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# replace with subset specified with one column replaced, another column not in subset
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# stays unchanged.
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row = self.sqlCtx.createDataFrame(
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[(u'Alice', 10, 10.0)], schema).replace(10, 20, subset=['name', 'age']).first()
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self.assertEqual(row.name, u'Alice')
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self.assertEqual(row.age, 20)
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self.assertEqual(row.height, 10.0)
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# replace with subset specified but no column will be replaced
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row = self.sqlCtx.createDataFrame(
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[(u'Alice', 10, None)], schema).replace(10, 20, subset=['name', 'height']).first()
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self.assertEqual(row.name, u'Alice')
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self.assertEqual(row.age, 10)
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self.assertEqual(row.height, None)
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class HiveContextSQLTests(ReusedPySparkTestCase):
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